Context: Many students now use generative AI in their coursework, yet its effects on intellectual development remain poorly understood. While prior work has investigated students' cognitive offloading during episodic interactions, it remains unclear whether using genAI routinely is tied to more fundamental shifts in students' thinking habits. Objective: We investigate (RQ1-How): how students' trust in and routine use of genAI affect their cognitive engagement -- specifically, reflection, need for understanding, and critical thinking in STEM coursework. Further, we investigate (RQ2-Who): which students are particularly vulnerable to these cognitive disengagement effects. Method: We drew on dual-process theory, cognitive offloading, and automation bias literature to develop a statistical model explaining how and to what extent students' trust-driven routine use of genAI affected their cognitive engagement habits in coursework, and how these effects differed across students' cognitive styles. We empirically evaluated this model using Partial Least Squares Structural Equation Modeling on survey data from 299 STEM students across five North American universities. Results: Students who trusted and routinely used genAI reported significantly lower cognitive engagement. Unexpectedly, students with higher technophilic motivations, risk tolerance, and computer self-efficacy -- traits often celebrated in STEM -- were more prone to these effects. Interestingly, prior experience with genAI or academia did not protect them from cognitively disengaging. Implications: Our findings suggest a potential cognitive debt cycle in which routine genAI use progressively weakens students' intellectual habits, potentially driving over-reliance and escalating usage. This poses critical challenges for curricula and genAI system design, requiring interventions that actively support cognitive engagement.

翻译：背景：目前许多学生在课程作业中使用生成式人工智能，但其对智力发展的影响仍不甚明晰。尽管已有研究调查了学生在阶段性互动中的认知卸载行为，但常规使用生成式人工智能是否与学生思维习惯的更根本性转变相关仍不明确。目标：本研究探讨（研究问题1-机制）：学生对生成式人工智能的信任与常规使用如何影响其认知投入——具体表现为STEM课程作业中的反思能力、理解需求与批判性思维。进一步探究（研究问题2-群体）：哪些学生特别容易受到此类认知脱离效应的影响。方法：我们整合双加工理论、认知卸载及自动化偏差研究，构建统计模型以解释学生基于信任的生成式人工智能常规使用如何（及在何种程度上）影响其课程学习中的认知投入习惯，并分析这些效应如何因学生的认知风格而异。通过对北美五所大学299名STEM专业学生的调查数据，采用偏最小二乘结构方程模型进行实证检验。结果：信任并常规使用生成式人工智能的学生表现出显著更低的认知投入水平。出乎意料的是，具有较高技术亲和动机、风险承受能力及计算机自我效能感的学生——这些通常在STEM领域备受推崇的特质——反而更容易受到此类效应影响。值得注意的是，先前在生成式人工智能或学术领域的经验并未使其免于认知脱离。启示：我们的研究结果揭示了一种潜在的认知负债循环，即常规使用生成式人工智能会逐渐削弱学生的智力习惯，可能导致过度依赖与使用强度升级。这对课程体系与生成式人工智能系统设计提出了严峻挑战，亟需能主动支持认知投入的干预措施。